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This invention relates to methods and algorithms that incorporate information from multiple imaging modalities to identify, estimate the size, and track the time course of brain lesions. Subjects develop brain lesions over the natural course of a disease. Currently, lesions are measured and tracked by a trained neuroradiologist using slice-by-slice inspection, a slow process that is prone to human error and hard to generalize to large observational studies.

This technology includes an automated and non-invasive assessment system called Automated Identification of subjects at risks of Multiple Sclerosis (AIMS) to assist clinicians in their diagnostic evaluation. A focus of AIMS is the ‘central vein sign’ (and other radiological findings).

This technology includes a system for automatic artifact-free measurement and visualization of tissue strain by MRI at native resolution. The investigation of regional soft tissue mechanical strain can serve as a unique indicator for different related disorders. For example, measurement of myocardial tissue during contraction can help calculate, track, and assess cardiac stress. Currently, methods such as tagging MRI (tMRI) are used for imaging soft tissue deformation. Despite being well validated, methods such as tMRI suffer from low spatial and temporal resolution.

This technology includes a versatile intravascular 3D intracardiac echocardiography (ICE) catheter that can operate under conventional X-ray and MRI for use during interventional cardiac procedures. The 3D MRICE and custom, GPU-based, real-time imaging system are also included. Structural heart disease affects more than 2.9% of the US population, and common interventional procedures can be difficult because of limitations in catheter devices and inadequate image guidance.

This technology includes a new technique to improve the fidelity of time-varying signals acquired in the dynamic contrast enhanced (DCE) imaging. This technique enhances the time-varying signals in a given DCE image series through deep convolutional neural networks (CNN) to learn the relationship of signal versus contrast concentration from other series of different contrast doses.